摘要
为解决协同制造环境下多协作企业的协同计划调度问题,针对多企业协同生产链实际运作过程,建立了一种考虑综合成本和完工时间的多目标计划调度优化模型。基于Pareto最优概念,采用NSGA-Ⅱ算法(快速非支配排序遗传算法)来解决多目标优化问题。为了保证解的收敛性和多样性,设计了有效的编解码方式和遗传操作程序,通过局部变异种群重复个体,并采用分布函数自适应选取精英数量,得到一系列Pareto最优解。最后通过仿真实例对多目标优化模型和算法进行了求解,结果表明,该方法可快速有效地实现全局多目标寻优,从而找到更多更合理的协同计划调度方案。
To solve the CPPS in the collaborative manufacturing environment,a multi-objective CPPS optimization model was developed including total cost and finish date according to the real production process in the multi-enterprise collaborative manufacture chain.A NSGA-Ⅱalgorithm was proposed and applied based on the Pareto optimality concept.In order to promote solution convergence and diversity,effective encoding,decoding and genetic operators were designed.The set of Pareto optimum solutions was obtained with partly mutating the overlapping individuals in the evolution populations and selecting the individual numbers of the elitism solution self-adapt by distribution function.A simulation experiment was carried out by using the proposed optimization model and algorithm.The results illustrate that the proposed method can solve the multi-objective CPPS fast and effectively and can find more reasonable CPPS solutions.
出处
《中国机械工程》
EI
CAS
CSCD
北大核心
2012年第5期563-569,共7页
China Mechanical Engineering
基金
国家高技术研究发展计划(863计划)资助项目(2007AA042002)
上海市"十一五"基础性重点研究资助项目(06JC14066)
关键词
协同制造
协同计划调度
多目标优化
PARETO最优
快速非支配排序遗传算法
collaborative manufacturing
collaborative production planning and scheduling（CPPS）
multiobjective optimization
Pareto optimality
NSGA-Ⅱ（non-dominated sorting genetic algorithm Ⅱ）
